Flow reduction due to arterial catheterization during stroke treatment - A computational study using a distributed compartment model.

The effectiveness of various stroke treatments depends on the anatomical variability of the cerebral vasculature, particularly the collateral blood vessel network. Collaterals at the level of the Circle of Willis and distal collaterals, such as the leptomeningeal arteries, serve as alternative avenues of flow when the primary pathway is obstructed during an ischemic stroke. Stroke treatment typically involves catheterization of the primary pathway, and the potential risk of further flow reduction to the affected brain area during this treatment has not been previously investigated. To address this clinical question, we derived the lumped parameters for catheterized blood vessels and implemented a corresponding distributed compartment (0D) model. This 0D model was validated against an experimental model and benchmark test cases solved using a 1D model. Additionally, we compared various off-center catheter trajectories modeled using a 3D solver to this 0D model. The differences between them were minimal, validating the simplifying assumption of the central catheter placement in the 0D model. The 0D model was then used to simulate blood flows in realistic cerebral arterial networks with different collateralization characteristics. Ischemic strokes were modeled by occlusion of the M1 segment of the middle cerebral artery in these networks. Catheters of different diameters were inserted up to the obstructed segment and flow alterations in the network were calculated. Results showed up to 45% maximum blood flow reduction in the affected brain region. These findings suggest that catheterization during stroke treatment may have a further detrimental effect for some patients with poor collateralization.

The complete mitochondrial genome of Meller's mongoose (Rhynchogale melleri).

Meller's mongoose (Rhynchogale melleri) is a member of the family Herpestidae (Mammalia: Carnivora) and the sole species in the genus Rhynchogale. It is primarily found in savannas and open woodlands of eastern sub-Saharan Africa. Here, we report the first complete mitochondrial genome for a female Meller's mongoose collected in Tanzania, generated using a genome-skimming approach. The mitogenome had a final length of 16,644 bp and a total of 37 annotated genes. Phylogenetic analysis validated the placement of this species in the herpestid subfamily Herpestinae. Ultimately, the outcomes of this research offer a genetic foundation for future studies of Meller's mongoose.

The complete mitochondrial genome and phylogenetic characterization of two putative subspecies of golden jackal (Canis aureus cruesemanni and Canis aureus moreotica).

The golden jackal (Canis aureus) is a canid species found across southern Eurasia. Several subspecies of this animal have been genetically studied in regions such as Europe, the Middle East, and India. However, one subspecies that lacks current research is the Indochinese jackal (Canis aureus cruesemanni), which is primarily found in Southeast Asia. Using a genome skimming approach, we assembled the first complete mitochondrial genome for an Indochinese jackal from Thailand. To expand the number of available Canis aureus mitogenomes, we also assembled and sequenced the first complete mitochondrial genome of a golden jackal from Turkey, representing the C. a. moreotica subspecies. The mitogenomes contained 37 annotated genes and are 16,729 bps (C. a. cruesemanni) and 16,669 bps (C. a. moreotica) in length. Phylogenetic analysis with 26 additional canid mitogenomes and analyses of a cytochrome b gene-only data set together support the Indochinese jackal as a distinct and early-branching lineage among golden jackals, thereby supporting its recognition as a possible subspecies. These analyses also demonstrate that the golden jackal from Turkey is likely not a distinct lineage due to close genetic relationships with golden jackals from India and Israel.

Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy.

Mice are the most commonly used model animals for itch research and for development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID (Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine. The best trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and a chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that could replace manual quantification for mouse scratching behavior in different itch models and for drug screening.